A Low-Complexity Neural Normalized Min-Sum LDPC Decoding Algorithm using Tensor-Train Decomposition

Compared with traditional low-density parity-check (LDPC) decoding algorithms, the current model-driven deep learning (DL)-based LDPC decoding algorithms face the disadvantage of high computational complexity. Based on the Neural Normalized Min-Sum (NNMS) algorithm, we propose a low-complexity model...

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Vydané v:	IEEE communications letters Ročník 26; číslo 12; s. 1
Hlavní autori:	Liang, Yuanhui, Lam, Chan-Tong, Ng, Benjamin K.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.12.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Artificial neural networks Bit error rate Complexity Computational complexity Decoding Decomposition Error correcting codes Iterative decoding Machine learning Mathematical analysis Model-Driven LDPC Decoding Neural Normalized Min-Sum Propagation losses Symbols Syndrome Loss Tensor-Train Decomposition Tensors
ISSN:	1089-7798, 1558-2558
On-line prístup:	Získať plný text
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